{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-07T04:18:01.771621900Z",
     "start_time": "2025-06-07T04:18:01.195406500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.903841963831796\n"
     ]
    },
    {
     "data": {
      "text/plain": "['xgb_model.pkl']"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import roc_auc_score, classification_report, roc_curve, auc\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import joblib\n",
    "from xgboost import XGBClassifier\n",
    "from src.zzm.config.config import FEATURE_NAMES\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 指定默认字体为黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像时负号 '-' 显示为方块的问题\n",
    "\n",
    "import os\n",
    "os.chdir(r'E:\\project\\python\\brain_drain_predict_project')\n",
    "\n",
    "# 读取数据\n",
    "data = pd.read_csv(\"data/raw/train.csv\")\n",
    "# 数据预处理  将有多分类的列 转化为 数值型的\n",
    "label_encoder = LabelEncoder()\n",
    "data = data.apply(label_encoder.fit_transform)\n",
    "\n",
    "# 选取特征列  12 + 10列\n",
    "# 上边通过卡方检验 选出了 12列\n",
    "# 下边通过 随机森林 + 置换重要性选出了 9列\n",
    "X = data[FEATURE_NAMES]\n",
    "y = data[\"Attrition\"]\n",
    "# 对X 标准化\n",
    "scale = StandardScaler()\n",
    "scale.fit(X)\n",
    "X = scale.transform(X)\n",
    "joblib.dump(scale, \"scale_model.pkl\")\n",
    "\n",
    "# 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=25)\n",
    "# 使用XGBClassifier 进行模型训练\n",
    "es_xgb = XGBClassifier(\n",
    "    learning_rate=0.3,\n",
    "    random_state=3,\n",
    "    n_estimators=350,\n",
    "    max_depth=1,  # 2\n",
    "    min_child_weight=2,\n",
    "    gamma=0,\n",
    "    reg_alpha=1,\n",
    "    reg_lambda=1.0,\n",
    "    objective='binary:logistic',\n",
    "    eval_metric='auc',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "es_xgb.fit(X_train, y_train)\n",
    "y_pred = es_xgb.predict_proba(X_test)[:, 1]\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)  # 或直接用roc_auc_score\n",
    "\n",
    "# 4. 绘制ROC曲线\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')  # 随机线\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.savefig(\"xgb_roc_curve.png\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# 打印分类评估报告\n",
    "# print(\"分类评估报告：\")\n",
    "# print(classification_report(y_test, y_pred))\n",
    "print(\"AUC:\", roc_auc_score(y_test, y_pred))  # AUC: 0.9005011257171909\n",
    "\n",
    "# 保存模型\n",
    "joblib.dump(es_xgb, \"xgb_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.903841963831796\n"
     ]
    },
    {
     "data": {
      "text/plain": "['xgb_model.pkl']"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_xgb = XGBClassifier(\n",
    "    learning_rate=0.3,\n",
    "    random_state=3,\n",
    "    n_estimators=350,\n",
    "    max_depth=1,  # 2\n",
    "    min_child_weight=2,\n",
    "    gamma=0,\n",
    "    reg_alpha=1,\n",
    "    reg_lambda=1.0,\n",
    "    objective='binary:logistic',\n",
    "    eval_metric='auc',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "es_xgb.fit(X_train, y_train)\n",
    "y_pred = es_xgb.predict_proba(X_test)[:, 1]\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)  # 或直接用roc_auc_score\n",
    "\n",
    "# 4. 绘制ROC曲线\n",
    "# 打印分类评估报告\n",
    "print(\"AUC:\", roc_auc_score(y_test, y_pred))  # AUC: 0.9005011257171909\n",
    "\n",
    "# 保存模型\n",
    "joblib.dump(es_xgb, \"xgb_model.pkl\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-07T04:28:35.290853400Z",
     "start_time": "2025-06-07T04:28:35.170318300Z"
    }
   },
   "id": "7459917a5b72f999"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "806f4660efd2fda0"
  }
 ],
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    "name": "ipython",
    "version": 2
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